Introduction to RF Planning A good plan should address the following Issues : Provision of required Capacity. Optimum usage of available frequency spectrum.

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Presentation on theme: "Introduction to RF Planning A good plan should address the following Issues : Provision of required Capacity. Optimum usage of available frequency spectrum."— Presentation transcript:

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Introduction to RF Planning A good plan should address the following Issues : Provision of required Capacity. Optimum usage of available frequency spectrum. Minimum number of sites. Provision for easy and smooth expansion of the Network in future. Provision of adequate coverage.

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Introduction to RF Planning In general a planning process starts with the inputs from the customer. The customer inputs include customer requirements, business plans, system characteristics, and any other constraints. After the planned system is implemented, the assumptions made during the planning process need to be validated and corrected wherever necessary through an optimization process. We can summarize the whole planning process under the 4 broad headings Capacity planning Coverage planning Parameter planning Optimization

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Okumara Hata Models In the early 1960, a Japanese scientist by name Okumara conducted extensive propagation tests for mobile systems at different frequencies. The test were conducted at 200, 453, 922, 1310, 1430 and 1920 Mhz. The test were also conducted for different BTS and mobile antenna heights, at each frequency, over varying distances between the BTS and the mobile. The Okumara tests were valid for : 150-2000 Mhz. 1-100 Kms. BTS heights of 30-200 m. MS antenna height, typically 1.5 m. (1-10 m.) The results of Okumara tests were graphically represented and were not easy for computer based analysis. Hata took Okumaras data and derived a set of empirical equations to calculate the path loss in various environments. He also suggested correction factors to be used in Quasi open and suburban areas.

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Non line of Sight Propagation Here we assume that the BTS antenna is above roof level for any building within the cell and that there is no line of sight between the BTS and the mobile We define the following parameters with reference to the diagram shown in the next slide: W the distance between street mobile and building Hm mobile antenna height h B BTS antenna height Hr height of roof h B difference between BTS height and roof top. Hm difference between mobile height and the roof top.

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Probability Density Function The study of radio signals involve actual measurement of signal levels at various points and applying statistical methods to the available data. A typical multipath signal is obtained by plotting the RSS for a number of samples. We divide the vertical scale in to 1 dB bin and count number of samples is plotted against RF level. This is how the probability density function for the receive signal is obtained. However, instead of such elaborate plotting we can use a statistical expression for the PDF of the RF signal given by : P(y) = [1/2 ] e [ - ( - y – m ) 2 / 2 ( ) 2 Where y is the random variable (the measured RSS in this case ), m is the mean value of the samples considered and y is the STANDARD DEVIATION of the measured signal with reference to the mean. The PDF obtained from the above is called a NORMAL curve or a Gaussian Distribution. It is always symmetrical with reference to the mean level.

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Probability Density Function Plotting the PDF : A PLOT OF RSS FOR A NUMBER OF SAMPLES

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Probability Density Function A PDF of random variable is given by : P(y) = [ ½ ] e [ - (y-m) 2 / 2( ) 2 ] Where, y is the variable, m is the mean value and is the Standard Deviation of the variable with reference to its mean value. The normal distribution (also called the Gaussian Distribution ) is symmetrical about the mean value. A typical Gaussian PDF :

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Probability Density Function The normal Distribution depends on the value of Standard Deviation We get a different curve for each value of The total area under the curve is UNITY

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Calculation of Standard Deviation If the mean of n samples is m, then the standard deviation is given by: = Square root of [{(x1-m) 2 + …..+( xn-m) 2 }/(n-1)] Where n is the number of samples and m is the mean. For our application we can re write the above equation as : = Square root of [{RSS1-RSS MEAN ) 2 +…..+(RSSN- RSS MEAN ) 2 /(N-1)}]

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Confidence Intervals The normal of the Gaussian distribution helps us to estimate the accuracy with which we can say that a measured value of the random variable would be within certain specified limits. The total area under the Normal curve is treated as unity. Then for any value of the measured value of the variable, its probability can be expressed as a percentage. In general, if m is mean value of the random variable within normal distribution and is the Standard Deviation, then, The probability of occurrence of the sample within m and any value of x of the variable is given by : P= By setting (x-m)/ = z, we get, P=

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Confidence Intervals The value of P is known as the Probability integral or the ERROR FUNCTION The limits (m n )are called the confidence intervals. From the formula given above, the probability P[(m- ) < z < (m+ )] = 68.26 % ; this means we are 68.34 % confident. P[(m- ) < z < (m+ )] = 95.44 % ; this means we are 95.44 % confident P[(m- ) < z < (m+ )] = 99.72 % ; this means we are 99.72 % confident. This is basically the area under the Normal Curve.

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The Concept of Normalized Standard Deviation The probability that a particular sample lies within specified limits is given by the equation : P= We define z = (x-m)/ as the Normalized Standard Deviation. The probability P could be obtained from Standard Tables (available in standard books on statistics ). A sample portion of the statistical table is presented in the next slide..

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Calculation of Fade Margin To calculate the fade margin we need to know : Propagation constant( ) >From formulae for the Model chosen >Or from the drive test plots Area probability : >A design objective usually 90 % Standard Deviation( ) >Calculated from the drive test results using statistical formulae or >Assumed for different environments. To use Jakes curves and tables.

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Calculation of Edge Probability and Fade Margin From the values of and we calculate : = / Find edge probability from Jakes curves for a desired coverage probability, against the value of on the x axis. Use Jakes table to find out the correlation factor required – Look for the column that has value closest to the edge probability and read the correlation factor across the corresponding row. Multiply by the correction factor to get the Fade Margin. Add Fade Margin to the RSS calculated from the power budget

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Significance Of Area and Edge Probabilities Required RSS is – 85 dBm. Suppose the desired coverage probability is 90 % and the edge probability from the Jakes curves is 0,75 This means that the mobile would receive a signal that is better than – 85 dBm in 90 % of the area of the cell At the edges of the cell, 75 % of the calls made would have this minimum signal strength (RSS).

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Fuzzy Maths and Fuzzy Logic The models that we studied so far are purely empirical. The formulas we used do not all take care of all the possible environments. Fuzzy logic could be useful for experienced planners in making right guesses. We divide the environment into 5 categories viz., Free space, Rural, Suburban, urban, and dense urban. We divide assign specific attenuation constant values to each categories, say Fuzzy logic helps us to guess the right value for, the attenuation constant for an environment which is neither rural nor suburban nor urban but a mixture, with a strong resemblance to one of the major categories. The following simple rules can be used : Mixture of Free space and Rural : Mixture of Rural and Suburban : Mixture of Suburban and Urban : Mixture of Urban and Dense urban :

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Cell Planning and C/I Issues The 2 major sources of interference are: Co Channel Interference. Adjacent Channel Interference. The levels of these Interference are dependent on The cell radius ® The distance cells (D) The minimum reuse distance (D) is given by : D = ( 3N ) ½ R Where N= Reuse pattern = i 2 + i j + j 2 Where I & j are integers.